The event synchronous canceller algorithm removes maternal ECG from abdominal signals without affecting the fetal ECG
Introduction
Fetal monitoring through evaluating the abdominal signals (ADS) allows the screening of the fetal well-being state, based on the analysis of the fetal heart rate (fHR) and of the waveform of the fetal ECG (fECG) [1]. The latter implies a strong advantage of this noninvasive recording method for fetal monitoring in comparison to methods like Doppler ultrasound [2]. But the main problem in extracting fECG from ADS is that the ADS contains several other disturbing signals besides the fECG which have higher amplitudes than the fECG and, in addition, are overlapping with it in the spectral domain. Some very recent publications [3], [4], [5], [6] underline that this topic currently attracts huge research efforts.
Among the “noise” signals in ADS, the maternal ECG (mECG) is clearly the most salient source of disturbance, since its R-peak shows amplitudes being 2–10 times greater than the amplitude of the fetal R-peak [7]. The fECG R-peak amplitude ranges from 10 to 100 μV [8]; it depends on the electrode configuration and varies due to the different body weight and size of the mother and due to the different positions of the fetus within the uterus. Other less disturbing signals which must be considered are the electronic noise (introduced by amplifiers, etc.), the baseline wander of signals, the myoelectric crosstalk from abdominal muscles, and the uterine activity, especially during labor. In addition, the access to the fECG changes with time, especially with the appearance of the vernix in the last three months of pregnancy, when conduction pathways between the fetal heart pace and the maternal abdomen are reduced from global radial propagation to some preferential directions [9]; then even the R-peak of the fECG is hardly detectable [10]. Obviously, any (even small) improvement in ADS processing for fECG extraction represents a step forward to overcome this difficulty.
Several methods have been proposed to extract the fECG from ADS for fHR analysis, such as principal component analysis [11], independent component analysis [12] and nonlinear projective filtering [13], but the increasing interest of physicians to consider not only the instantaneous fHR but also the waveform of the fECG introduces new requirements for preserving the fECG morphology in ADS processing, thus linear filtering methods in general are getting more focused. For example, adaptive noise canceling using standard mECG recordings in addition [14] and the event-synchronous interference canceller (ESC) method [15], [16] are appropriate candidates for removing the mECG from ADS to enhance the fECG. Here, the ESC approach which was previously [16] evaluated only on the DaISy [17] abdominal channel having the highest amplitude of the fECG (first abdominal channel) will now be evaluated on real data with different morphologies recorded during pregnancy and labor, comprising also the second channel of the DaISy dataset [17] showing a small fetal QRS as compared with the maternal QRS, with an amplitude of about the same value as the amplitude of the maternal T-wave, in order to have included a well known dataset available to the research community. Finally, the performance of the ESC algorithm is discussed, in particular as a possible preprocessing method applied before any extended computerized fECG analysis for detailed fECG morphology evaluation.
Section snippets
Fetal ECG enhancement by the event-synchronous interference canceller (ESC) application
The classical adaptive noise canceller (Fig. 1a) removes a disturbing crosstalk signal from the signal of interest, under the assumptions that the signal of interest, s(n), is not correlated with the noise source, , and that a version of the noise source is available through another recording channel, , which is used as a reference signal in this adaptive noise canceling concept. These conditions allow to estimate the crosstalk transfer function , which in turn can be used to
Performance of the ESC determined with real data, recorded during pregnancy and labor
The ESC method is now tested on real ADS, recorded during pregnancy and labor, with and without medication during labor.
The first example, shown in Fig. 2a, represents the ADS recorded during pregnancy, 37 wk, from a sport woman having a no risk pregnancy. Clearly, the fECG is now the dominant component in the processed ADS (Fig. 2b).
Another example demonstrating the performance of the ESC in mECG removal from ADS is shown in Fig. 3. The ADS used here is a channel from the data set offered by De
Discussions and conclusions
These examples demonstrated that the ESC cancels the mECG in the ADS very effectively, almost without distortion of the other ADS components. The ESC shows a very good extracted fECG, conserving the shape and the amplitude of the fECG recorded in the ADS due to the basic compensation concept [14]. Interestingly to note that the ESC algorithm (basically a subtraction algorithm) is modest with respect to computational load. Certainly, there are several different problems to be solved beyond the
Conclusion
In summary, the ESC basically proved to be a suitable ADS processor which preserves the fECG morphology for its subsequent detailed analysis [1]. Certainly, fECG morphology analysis and possible contribution to clinical aspects are in the onset status, but signal processing technology necessary to perform it must be available before clinical evaluation can be achieved. The ESC as demonstrated by the mECG removal in this paper turns out to be appropriate for such a future signal processing
Conflict of interest statement
None declared.
Acknowledgment
The authors are indebted to Dragos Taralunga for his help in preparing the manuscript. The study was supported by the Deutsche Forschungsgemeinschaft (DFG) research Grant 436 RUM 113/29/0-1, by the National University Research Council of Romania (CNCSIS) under the National Research Grant A 353/2005-48 and A 146/2007, and by the Dutch Technology Foundation STW research Grant EGT 6480.
G. Mihaela Ungureanu received the B.Eng. in Applied Electronics in 1995, the M.Sc. in Computer Science in 1996 and the Ph.D. in 2002, all from the Politehnica University of Bucharest, Romania. Now she is Associate Professor at Politehnica University of Bucharest, Applied Electronics and Information Engineering Department. In 2003 she has received a ten months post-doctoral scholarship from the DAAD (German Academic Exchange service). In 2004 she has received another fellowship from the NWO
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G. Mihaela Ungureanu received the B.Eng. in Applied Electronics in 1995, the M.Sc. in Computer Science in 1996 and the Ph.D. in 2002, all from the Politehnica University of Bucharest, Romania. Now she is Associate Professor at Politehnica University of Bucharest, Applied Electronics and Information Engineering Department. In 2003 she has received a ten months post-doctoral scholarship from the DAAD (German Academic Exchange service). In 2004 she has received another fellowship from the NWO (Netherlands Organization for Scientific Research). In 2006 she received Fulbright Senior Research Award. In 2007 she received an Intra-European Marie Curie Fellowship. Her research interests include signal processing and artificial intelligence applied in biomedical field and computer programming.
Johannes W.M. Bergmans (SM’91) received the degree of Elektrotechnisch Ingenieur, cum laude, in 1982 and the Ph.D. degree in 1987, both from Eindhoven University of Technology, Eindhoven, The Netherlands. From 1982 to 1999 he was with Philips Research Laboratories, Eindhoven, The Netherlands, working on signal-processing techniques and IC-architectures for digital transmission and recording systems. In 1988 and 1989 he was exchange researcher at Hitachi Central Research Labs, Tokyo, Japan. Since 1999 he is professor and chairman of the signal processing systems group at Eindhoven University of Technology, Eindhoven, The Netherlands. He has published extensively in refereed journals, has authored a book ‘Digital Baseband Transmission and Recording’ (Boston: Kluwer Academic Publishers, 1996), and holds around 40 US patents. From 1986 to 1992 Prof. Bergmans was a board member of the Dutch Electronics and Radio Society and of the Dutch URSI Committee. Since 1999 he is Treasurer of the IEEE Benelux Section.
S. Guid Oei received the degree of Medical Doctor in 1986, and the Ph.D. degree in 1996, both from Leiden University. In 1995 he was registered as gynaecologist. In 1997 he received a subspecialty training in perinatology at Flinders Medical Centre in Adelaide, South Australia. Since 1996 he is member of the staff of the department of obstetrics and gynaecology of Máxima Medical Centre in Eindhoven, the Netherlands. Since 2003 he is professor of fundamental perinatology at the University of Technology Eindhoven. In 2006 he was appointed dean of the MMC Academy. Prof. Dr. Oei is chairman of the Dutch Working Party on Perinatology and Maternal Medicine, chairman of the Dutch Society for Simulation in Healthcare and board member of the Dutch Society for Perinatal Medicine.
Alexandru Ungureanu received the B.Sc. in Applied Mathematics in 1996, the M.Sc. in Numerical Methods in 1997, and the Ph.D. in 2009, all from the student at Politehnica University of Bucharest.
Werner Wolf received his M.S.E.E. in 1970 and his Ph.D. in 1978, both from the Technical University Munich, Germany. In 1978, he moved to the Institute of Mathematics and Computer Science heading the Biosignal Processing Lab. His main interests are processing of biosignals (e.g. evoked potentials in the electroencephalogram, the electromyogram, etc.) and includes basic research in sensorimotor systems (eye movements, hand and arm movements, motor control, etc.) as well as applied research in sport sciences and ergonomics. He is author of more than 200 scientific publications, and a member of the IEEE Society of Engineering in Medicine and Biology, the IEEE Society of Signal Processing, the American Society of Neuroscience, and several national engineering societies. Professor Wolf is a member of the Faculty of Electrical Engineering of the University of the Armed Forces in Munich and gives courses on Neuronal Information Processing, Advanced Information Processing Systems and Biosignal Processing. Actually, he is Program Officer of the IEEE German EMBS Chapter.